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Elucidation of principle of neural circuits using small circuits

開催日 2014/9/12
時間 17:10 - 19:10
会場 Room F(302)
Chairperson(s) 上川内 あづさ / Azusa Kamikouchi (名古屋大学大学院理学研究科 生命理学専攻 / Graduate School of Sciene, Nagoya University, Japan)
森 郁恵 / Ikue Mori (名古屋大学大学院理学研究科 / Graduate School of Sciene, Nagoya University, Japan)

Computation of Behavior by Whole-Brain Dynamics

  • S2-F-3-1
  • Saul Kato:1 Harris S Kaplan:1 Tina Schrödel:1 Manuel Zimmer:1 
  • 1:IMP Research Institute of Molecular Pathology, Austria 

In single-celled organisms such as E. coli, we have a basic understanding of how the time evolution of the multi-dimensional internal state of the system, as embodied by concentrations of molecules, determines the real-time behavior of the organism. No such understanding exists for multi-cellular organisms with nervous systems, where the time evolution of the activity of many interacting neurons determines the real-time motor behavior of the organism. Recently, the ability to densely record the activity of the entire brain of C. elegans has provided an unprecedented view of the evolving, high-dimensional internal neural state of an animal. Coupled with studies of individual neural activity in freely moving animals, we have been able to make progress in elucidating the neural code of behavior and understanding the origin of behavioral transitions. Surprisingly, we find that a large portion of neurons synchronize their activity and these synchronized network states encode motor state in an apparently redundant manner; however, subtle differences in activity traces indicate that each neuron also carries its own information. This multiplexing of information may allow the nervous system to encode a high degree of sensory and state information while still producing robust, holistic output. We further find that sensory input influences transition probabilities between states but only subtly modulates the geometry of state space trajectories. A picture is emerging of how discrete behavior is generated by a nervous system: one of a smoothly evolving internal state trajectory transitioning between attractor basins due to intrinsic dynamical structure and perturbation by sensory input.

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